Comparison Gallery
Reconstruction
We provided the reconstruction results of baseline methods[Bagher et al., Sun et al., Hu et al.] and our networks(NPs model with different setups, hypernet). We rendered the reconstruction BRDF under three different environment maps(St, Grace, Uffizi).
Links | Description |
---|---|
BagHer-Grace
BagHer-Uffizi BagHer-St |
[Bagher et al.] |
Hu-Grace
Hu-Uffizi Hu-St |
[Hu et al.] |
Sun-Grace
Sun-Uffizi Sun-St |
[Sun et al.] with 1 diffuse lobe and 5 specular lobes |
Hypernet-Grace
Hypernet-Uffizi Hypernet-St |
our hypernet with 4 times log mappings, 7-dimensional latent space, and mean aggregator |
FinalNPs-Grace
FinalNPs-Uffizi FinalNPs-St |
our NPs model with 4 times log mappings, 7-dimensional latent space, and mean aggregator |
NPsLOG2DIM7-Grace
NPsLOG2DIM7-Uffizi NPsLOG2DIM7-St |
our NPs model with 2 times log mappings, 7-dimensional latent space, and mean aggregator |
NPsLOG3DIM7-Grace
NPsLOG3DIM7-Uffizi NPsLOG3DIM7-St |
our NPs model with 3 times log mappings, 7-dimensional latent space, and mean aggregator |
NPsLOG4DIM2-Grace
NPsLOG4DIM2-Uffizi NPsLOG4DIM2-St |
our NPs model with 4 times log mappings, 2-dimensional latent space, and mean aggregator |
NPsLOG4DIM3-Grace
NPsLOG4DIM3-Uffizi NPsLOG4DIM3-St |
our NPs model with 4 times log mappings, 3-dimensional latent space, and mean aggregator |
NPsLOG4DIM4-Grace
NPsLOG4DIM4-Uffizi NPsLOG4DIM4-St |
our NPs model with 4 times log mappings, 4-dimensional latent space, and mean aggregator |
NPsLOG4DIM5-Grace
NPsLOG4DIM5-Uffizi NPsLOG4DIM5-St |
our NPs model with 4 times log mappings, 5-dimensional latent space, and mean aggregator |
NPsLOG4DIM6-Grace
NPsLOG4DIM6-Uffizi NPsLOG4DIM6-St |
our NPs model with 4 times log mappings, 6-dimensional latent space, and mean aggregator |
Importance Sampling
We compared three importance sampling strategies(ours, cosine-weighted, GGX-based) under four environment maps.
ImportanceSampling-GraceImportanceSampling-Uffizi
ImportanceSampling-St
ImportanceSampling-envmap
Moreover, to validate that our NICE network works well on interpolated materials, we interpolated each materials in the MERL dataset with one of its neighborings randomly, and rendered them using two importance sampling techniques(ours, cosine).
ImportanceSampling-interpolation-envmapHypernet Interpolation
To validate the interpolation ability of our hypernet, we randomly interpolated the materials, similar to the handling in importance sampling. Then we compared the renderings of the BRDFs reconstructed using our NPs model and our hypernet.
HypernetInterpolation